Dreamers’ evaluation of the emotional valence of their day-to-day dreams is indicative of some mood regulation function
Bibliographic record
Abstract
Dreams may contribute to psychological adaptation by aiding in mood regulation. One way it could be achieved is through a desensitization process whereby negative events are replayed within the dream under lower conditions of negative emotionality. Evidence of this theory is supported by the tendency of dreamers to evaluate their emotions felt in their dreams more positively compared to an independent judge (i.e., positivity bias). Additionally, it has been observed that while dream emotions are typically more negative than pre-sleep emotions, morning emotions are more positive, suggesting that emotional regulation occurs overnight and may help improve mood in the morning. The present study aimed to examine the relationships between pre-sleep, dream, and morning mood and the potential desensitization function of remembered dreams as indicated by their effects on morning mood and stress. Methodology: Participants (N = 188; Mean age = 19.2, SD = 3.0) recorded their dreams (N = 345 dreams) and self-reported their stress and mood at bedtime, during their dream retrospectively, and upon waking. A judge also evaluated the subjects’ dream moods. Subjects’ positivity bias was defined as the difference between the subjects and the judge’s evaluation of the positive emotions in the dream. Results: A MANOVA revealed that subjects perceived a higher level of positive emotions in their dreams compared to a judge. Multi-group path analysis revealed that some relationships between pre-sleep, dream, and morning emotions and stress differed in positive and negative dream nights. In both groups, the strongest predictors of morning mood and stress were pre-sleep mood and stress, respectively. The second strongest predictor of positive morning mood was the subjects’ dream positivity bias. Conclusion: Results provide some support for the association of dreaming in mood regulation attributable to REM sleep. They also highlight that pathways implicated in mood regulation may be distinct from stress regulation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".